COMP 551 Winter 2025: Applied Machine Learning (4 credits)

Course Overview

This course covers a selected set of topics in machine learning and data mining, with an emphasis on good methods and practices for deployment of real systems. The majority of sections are related to commonly used supervised learning techniques, and to a lesser degree unsupervised methods. This includes fundamentals of algorithms on linear and logistic regression, decision trees, neural networks, clustering, as well as key techniques for feature selection and dimensionality reduction, error estimation and empirical validation.

Class location and time:

Location: Strathcona Anatomy and Dentistry M-1
Time: Tuesday, Thursday 10:05-11:25
Duration: January 7- April 10, 2024 (14 weeks)

Prerequisite/recommended

Required: MATH 323 or ECSE 205, COMP 202, MATH 133, MATH 222 (or their equivalents)
Restriction(s): Not open to students who have taken or are taking COMP 451, ECSE 551, or PSYC 560.
Recommended: COMP-424 or ECSE-526

Instructor Office Hours

Tue, Thu 11:30-12 pm

Teaching Assistant - Office Hours

Charlotte Volk - TBD
Huiliang Zhang - TBD
Rafid Saif - TBD
Rahma Nouaji - TBD
Sara Tavakoli - TBD
Yijie Zhang - TBD
Yuting Song - TBD
Zahra Tehraninasab - TBD

Details of office hours locations or zoom links are available on MyCourses Class Resource Links

Evaluation

Quizzes (10%): 10 quizzes each worth 1%
Midterm exam (20%): in-person written exam
Assignments (40%): 4 group class assignments (3 students per group) as 4 mini-projects each worth 10%. All of the quesions are programming questions in Python
Final exam (30%): in-person written exam

Recommended/Complementary Textbooks (all available online)

Probabilistic Machine Learning: An Introduction (2022) by Kevin Murphy (Murphy22)
Machine Learning: a probabilsitic perspective (2016) by Kevin Murphy (Murphy16)
Pattern recognition and machine learning (2006) by Christopher Bishop (Bishop06)
Deep Learning (2016) by Ian Goodfellow, Yoshua Bengio, and Aaron Courville (Goodfellow16).

Course coverage:

Machine learning fundamentals (2 weeks):

Objective functions
Model evaluation

Non-parametric methods (1 week)

K-nearest neighbor
Classification and regression tree

Linear methods (3 weeks):

Linear regression
Logistic and multinomial regression
Regularized linear regression
Probabilistic interpretations

Deep learning methods (4 weeks)

Multilayer perceptrons
Convolutional neural networks
Recurrent neural networks

Unsupervised learning (1 weeks)

Clustering
Principal component analysis
Autoencoder